2134/37229
Yang Zhou
Yang
Zhou
Jiangtao Wang
Jiangtao
Wang
Baihua Li
Baihua
Li
Qinggang Meng
Qinggang
Meng
Emanuele Rocco
Emanuele
Rocco
Andrea Saiani
Andrea
Saiani
Underwater scene segmentation by deep neural network
Loughborough University
2019
untagged
Information and Computing Sciences not elsewhere classified
2019-03-18 14:48:59
Conference contribution
https://repository.lboro.ac.uk/articles/conference_contribution/Underwater_scene_segmentation_by_deep_neural_network/9405458
A deep neural network architecture is proposed in
this paper for underwater scene semantic segmentation. The
architecture consists of encoder and decoder networks. Pretrained VGG-16 network is used as a feature extractor, while the
decoder learns to expand the lower resolution feature maps. The
network applies max un-pooling operator to avoid large number
of learnable parameters, and, in order to make use of the feature
maps in encoder network, it concatenates the feature maps with
decoder and encoder for lower resolution feature maps. Our
architecture shows capabilities of faster convergence and better
accuracy. To get a clear view of underwater scene, an underwater
enhancement neural network architecture is described in this
paper and applied for training. It speeds up the training process
and convergence rate in training.